The overlap across psychotic disorders: A functional network connectivity analysis.

Functional network connectivity (FNC) has previously been shown to distinguish patient groups from healthy controls (HC). However, the overlap across psychiatric disorders such as schizophrenia (SZ), bipolar (BP), and schizoaffective disorder (SAD) is not evident yet. This study focuses on studying the overlap across these three psychotic disorders in both dynamic and static FNC (dFNC/sFNC). We used resting-state fMRI, demographics, and clinical information from the Bipolar-Schizophrenia Network on Intermediate Phenotypes cohort (BSNIP). The data includes three groups of patients with schizophrenia (SZ, N = 181), bipolar (BP, N = 163), and schizoaffective (SAD, N = 130) and HC (N = 238) groups. After estimating each individual's dFNC, we group them into three distinct states. We evaluated two dFNC features, including occupancy rate (OCR) and distance travelled over time. Finally, the extracted features, including both sFNC and dFNC, are tested statistically across patients and HC groups. In addition, we explored the link between the clinical scores and the extracted features. We evaluated the connectivity patterns and their overlap among SZ, BP, and SAD disorders (false discovery rate or FDR corrected p < 0.05). Results showed dFNC captured unique information about overlap across disorders where all disorder groups showed similar pattern of activity in state 2. Moreover, the results showed similar patterns between SZ and SAD in state 1 which was different than BP. Finally, the distance travelled feature of SZ (average R = 0.245, p < 0.01) and combined distance travelled from all disorders was predictive of the PANSS symptoms scores (average R = 0.147, p < 0.01).

The Role of Stimuli-Driven and Goal-Driven Attention in Shopping Decision-Making Behaviors-An EEG and VR Study.

The human attention system, similar to other networks in the brain, is of a complex nature. At any moment, our attention can shift between external and internal stimuli. In this study, we aimed to assess three EEG-based measures of attention (Power Spectral Density, Connectivity, and Spectral Entropy) in decision-making situations involving goal-directed and stimulus-driven attention using a Virtual Reality supermarket. We collected the EEG data of 29 participants in 2 shopping phases, planned and unplanned purchases. The three mentioned features were extracted and a statistical analysis was conducted. We evaluated the discriminatory power of these features using an SVM classifier. The results showed a significant (p-value < 0.001) increase in theta power over frontal, central, and temporal lobes for the planned purchase phase. There was also a significant decrease in alpha power over frontal and parietal lobes in the unplanned purchase phase. A significant increase in the frontoparietal connectivity during the planned purchase was observed. Additionally, an increase in spectral entropy was observed in the frontoparietal region for the unplanned purchase phase. The classification results showed that spectral entropy has the highest discriminatory power. This study can provide further insights into the attentional behaviors of consumers and how their type of attentional control can affect their decision-making processes.

Motivation in the metaverse: A dual-process approach to consumer choices in a virtual reality supermarket.

Introduction: Consumer decision-making processes involve a complex interrelation between perception, emotion, and cognition. Despite a vast and diverse literature, little effort has been invested in investigating the neural mechanism behind such processes. Methods: In the present work, our interest was to investigate whether asymmetrical activation of the frontal lobe of the brain could help to characterize consumer's choices. To obtain stronger experimental control, we devised an experiment in a virtual reality retail store, while simultaneously recording participant brain responses using electroencephalogram (EEG). During the virtual store test, participants completed two tasks; first, to choose items from a predefined shopping list, a phase we termed as "planned purchase". Second, subjects were instructed that they could also choose products that were not on the list, which we labeled as "unplanned purchase." We assumed that the planned purchases were associated with a stronger cognitive engagement, and the second task was more reliant on immediate emotional responses. Results: By analyzing the EEG data based on frontal asymmetry measures, we find that frontal asymmetry in the gamma band reflected the distinction between planned and unplanned decisions, where unplanned purchases were accompanied by stronger asymmetry deflections (relative frontal left activity was higher). In addition, frontal asymmetry in the alpha, beta, and gamma ranges illustrate clear differences between choices and no-choices periods during the shopping tasks. Discussion: These results are discussed in light of the distinction between planned and unplanned purchase in consumer situations, how this is reflected in the relative cognitive and emotional brain responses, and more generally how this can influence research in the emerging area of virtual and augmented shopping.